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To investigate protein evolution by detecting suppressed epitope structures.

ABSTRACT: Material remains of ancestor nucleotides and proteins are largely unavailable, thus sequence comparison among homologous genes in present-day organisms forms the core of current knowledge of molecular evolution. Variation in protein three-dimensional structure is a basis for functional diversity. To study the evolution of three-dimensional structures in related proteins would significantly improve our understanding of protein evolution and function. A protein may contain ancestor conformations that have been allosterically suppressed by evolutionarily additive structures. Using monoclonal antibody probes to detect such conformation in proteins after removing the suppressor structure, our study demonstrates three-dimensional structure evidence for the evolutionary relationship between troponin I and troponin T, two subunits of the troponin complex in the Ca(2+)-regulatory system of striated muscle, and among their muscle type-specific isoforms. The experimental data show the feasibility of detecting evolutionarily suppressed history-telling structural states in proteins by removing conformational modulator segments added during evolution. In addition to identifying structural modifications that were critical to the emergence of diverged proteins, investigating this novel mode of evolution will help us to understand the origin and functional potential of protein structures.

SUBMITTER: Chong SM

PROVIDER: S-EPMC2752406 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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To investigate protein evolution by detecting suppressed epitope structures.

Chong Stephen M SM Jin J-P JP

Journal of molecular evolution 20090414 5

Material remains of ancestor nucleotides and proteins are largely unavailable, thus sequence comparison among homologous genes in present-day organisms forms the core of current knowledge of molecular evolution. Variation in protein three-dimensional structure is a basis for functional diversity. To study the evolution of three-dimensional structures in related proteins would significantly improve our understanding of protein evolution and function. A protein may contain ancestor conformations t ...[more]

PMID: 19365646

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Project description:Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

Dataset Information

To investigate protein evolution by detecting suppressed epitope structures.

Publications

To investigate protein evolution by detecting suppressed epitope structures.

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