Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1142/S0219720011005306 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Protein folding,folding rate prediction,neural network,genetic algorithm | Correlation coefficient,Protein structure prediction,Protein folding,Jackknife resampling,Biology,Amino acid,Statistical potential,Bioinformatics,Protein structure,Peptide sequence | Journal |
Volume | Issue | ISSN |
9 | 1 | 0219-7200 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianxiu Guo | 1 | 11 | 1.94 |
| Nini Rao | 2 | 85 | 11.36 |