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• W2036700590 abstract "Predicting protein folding rate from amino acid sequence is an important challenge in computational and molecular biology. Over the past few years, many methods have been developed to reflect the correlation between the folding rates and protein structures and sequences. In this paper, we present an effective method, a combined neural network--genetic algorithm approach, to predict protein folding rates only from amino acid sequences, without any explicit structural information. The originality of this paper is that, for the first time, it tackles the effect of sequence order. The proposed method provides a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.80 and the standard error is 2.65 for 93 proteins, the largest such databases of proteins yet studied, when evaluated with leave-one-out jackknife test. The comparative results demonstrate that this correlation is better than most of other methods, and suggest the important contribution of sequence order information to the determination of protein folding rates." @default.
• W2036700590 created "2016-06-24" @default.
• W2036700590 creator A5015639981 @default.
• W2036700590 creator A5078493563 @default.
• W2036700590 date "2011-02-01" @default.
• W2036700590 modified "2023-09-25" @default.
• W2036700590 title "PREDICTING PROTEIN FOLDING RATE FROM AMINO ACID SEQUENCE" @default.
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• W2036700590 doi "https://doi.org/10.1142/s0219720011005306" @default.
• W2036700590 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21328704" @default.
• W2036700590 hasPublicationYear "2011" @default.
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