amino acid
; globular protein
; protein
; solvent
; protein
; amino acid sequence
; Article
; atom
; conformational transition
; controlled study
; energy
; entropy
; hydrophobicity
; knowledge base
; polarization
; priority journal
; protein conformation
; protein engineering
; protein secondary structure
; protein stability
; chemical phenomena
; chemistry
; protein folding
; Amino Acids
; Entropy
; Hydrophobic and Hydrophilic Interactions
; Protein Conformation
; Protein Folding
; Proteins
; Solvents
英文摘要:
Evaluating protein structures requires reliable free energies with good estimates of both potential energies and entropies. Although there are many demonstrated successes from using knowledge-based potential energies, computing entropies of proteins has lagged far behind. Here we take an entirely different approach and evaluate knowledge-based conformational entropies of proteins based on the observed frequencies of contact changes between amino acids in a set of 167 diverse proteins, each of which has two alternative structures. The results show that charged and polar interactions break more often than hydrophobic pairs. This pattern correlates strongly with the average solvent exposure of amino acids in globular proteins, as well as with polarity indices and the sizes of the amino acids. Knowledgebased entropies are derived by using the inverse Boltzmann relationship, in a manner analogous to the way that knowledge-based potentials have been extracted. Including these new knowledge-based entropies almost doubles the performance of knowledge-based potentials in selecting the native protein structures from decoy sets. Beyond the overall energy-entropy compensation, a similar compensation is seen for individual pairs of interacting amino acids. The entropies in this report have immediate applications for 3D structure prediction, protein model assessment, and protein engineering and design.
Sankar, K., Bioinformatics and Computational Biology Interdepartmental Program, Iowa State University, Ames, IA 50011, United States, Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, United States; Jia, K., Bioinformatics and Computational Biology Interdepartmental Program, Iowa State University, Ames, IA 50011, United States, Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, United States; Jernigan, R.L., Bioinformatics and Computational Biology Interdepartmental Program, Iowa State University, Ames, IA 50011, United States, Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, United States, L. H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, United States
Recommended Citation:
Sankar K.,Jia K.,Jernigan R.L.. Knowledge-based entropies improve the identification of native protein structures[J]. Proceedings of the National Academy of Sciences of the United States of America,2017-01-01,114(11)