A new computational model for protein folding based on atomic solvation.
A new model for calculating the solvation energy of proteins is developed and tested for its ability to identify the native conformation as the global energy minimum among a group of thousands of computationally generated compact non-native conformations for a series of globular proteins. In the model (called the WZS model), solvation preferences for a set of 17 chemically derived molecular fragments of the 20 amino acids are learned by a training algorithm based on maximizing the solvation energy difference between native and non-native conformations for a training set of proteins. The performance of the WZS model confirms the success of this learning approach; the WZS model misrecognizes (as more stable than native) only 7 of 8,200 non-native structures. Possible applications of this model to the prediction of protein structure from sequence are discussed.
Wang
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Department of Molecular Biology, Jilin University, Changchun, People's Republic of China.
Zhang
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Scott
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D000465:Algorithms; D008433:Mathematics; D008956:Models, Chemical; D008958:Models, Molecular; D015394:Molecular Structure; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012996:Solutions; D013816:Thermodynamics
D011506:Proteins; D012996:Solutions