Dataset Information

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.

ABSTRACT: The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. In this study we use a statistical inference technique inspired by statistical physics, which builds a coevolutionary "Potts" Hamiltonian model of sequence variation in a protein family. We show how this model has sufficient power to predict the probability of specific subsequences in the highly diverged kinase family, which we verify by comparing the model's predictions with experimental observations in the Uniprot database. We show that the pairwise (residue-residue) interaction terms of the statistical model are necessary and sufficient to capture higher-than-pairwise mutation patterns of natural kinase sequences. We observe that previously identified functional sets of residues have much stronger correlated interaction scores than are typical.

SUBMITTER: Haldane A

PROVIDER: S-EPMC5773752 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.

Haldane Allan A Flynn William F WF He Peng P Levy Ronald M RM

Biophysical journal 20180101 1

The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. I ...[more]

PMID: 29320688

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Project description:BackgroundBeta-lactamases are one of the most serious threats to public health. In order to combat this threat we need to study the molecular and functional diversity of these enzymes and identify signatures specific to these enzymes. These signatures will enable us to develop inhibitors and diagnostic probes specific to lactamases. The existing classification of beta-lactamases was developed nearly 30 years ago when few lactamases were available. DLact database contain more than 2000 beta-lactamase, which can be used to study the molecular diversity and to identify signatures specific to this family.MethodsA set of 2020 beta-lactamase proteins available in the DLact database http://59.160.102.202/DLact were classified using graph-based clustering of Best Bi-Directional Hits. Non-redundant (> 90 percent identical) protein sequences from each group were aligned using T-Coffee and annotated using information available in literature. Motifs specific to each group were predicted using PRATT program.ResultsThe graph-based classification of beta-lactamase proteins resulted in the formation of six groups (Four major groups containing 191, 726, 774 and 73 proteins while two minor groups containing 50 and 8 proteins). Based on the information available in literature, we found that each of the four major groups correspond to the four classes proposed by Ambler. The two minor groups were novel and do not contain molecular signatures of beta-lactamase proteins reported in literature. The group-specific motifs showed high sensitivity (> 70%) and very high specificity (> 90%). The motifs from three groups (corresponding to class A, C and D) had a high level of conservation at DNA as well as protein level whereas the motifs from the fourth group (corresponding to class B) showed conservation at only protein level.ConclusionThe graph-based classification of beta-lactamase proteins corresponds with the classification proposed by Ambler, thus there is no need for formulating a new classification. However, further characterization of two small groups may require updating the existing classification scheme. Better sensitivity and specificity of group-specific motifs identified in this study, as compared to PROSITE motifs, and their proximity to the active site indicates that these motifs represents group-specific signature of beta-lactamases and can be further developed into diagnostics and therapeutics.

Dataset Information

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.

Publications

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.

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