scholarly journals Prediction of protein folding rates from the amino acid sequence-predicted secondary structure

2004 ◽  
Vol 101 (24) ◽  
pp. 8942-8944 ◽  
Author(s):  
Dmitry N. Ivankov ◽  
Alexei V. Finkelstein
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Folding Rates

scholarly journals FOLD-RATE: prediction of protein folding rates from amino acid sequence

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W70-W74 ◽  
Author(s):  
M. M. Gromiha ◽  
A. M. Thangakani ◽  
S. Selvaraj
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Folding Rates ◽  
Rate Prediction

Swfoldrate: Predicting protein folding rates from amino acid sequence with sliding window method

2012 ◽  
Vol 81 (1) ◽  
pp. 140-148 ◽  
Author(s):  
Xiang Cheng ◽  
Xuan Xiao ◽  
Zhi-cheng Wu ◽  
Pu Wang ◽  
Wei-zhong Lin
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Sliding Window ◽  
Folding Rates ◽  
Window Method ◽  
Sliding Window Method

PREDICTING PROTEIN FOLDING RATE FROM AMINO ACID SEQUENCE

2011 ◽  
Vol 09 (01) ◽  
pp. 1-13 ◽  
Author(s):  
JIANXIU GUO ◽  
NINI RAO
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Structural Information ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Folding Rate ◽  
Jackknife Test ◽  
Folding Rates ◽  
Protein Folding Rate

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.

Predicting Protein Folding Rate From Amino Acid Sequence

2011 ◽  
Vol 37 (12) ◽  
pp. 1331-1338 ◽  
Author(s):  
Jian-Xiu GUO ◽  
Ni-Ni RAO ◽  
Guang-Xiong LIU ◽  
Jie LI ◽  
Yun-He WANG
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Folding Rate ◽  
Protein Folding Rate

Protein Folding Kinetic Order Prediction from Amino Acid Sequence Based on Horizontal Visibility Network

2016 ◽  
Vol 11 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Zhi-Qin Zhao ◽  
Zu-Guo Yu ◽  
Vo Anh ◽  
Jing-Yang Wu ◽  
Guo-Sheng Han
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Horizontal Visibility ◽  
Kinetic Order

scholarly journals Prediction of protein folding class using global description of amino acid sequence.

1995 ◽  
Vol 92 (19) ◽  
pp. 8700-8704 ◽  
Author(s):  
I. Dubchak ◽  
I. Muchnik ◽  
S. R. Holbrook ◽  
S. H. Kim
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence

Shark Myelin Basic Protein: Amino Acid Sequence, Secondary Structure, and Self-Association

1990 ◽  
Vol 55 (3) ◽  
pp. 950-955 ◽  
Author(s):  
Trudy J. Milne ◽  
Annette R. Atkins ◽  
Juanita A. Warren ◽  
Wendy P. Auton ◽  
Ross Smith
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Myelin Basic Protein ◽  
Basic Protein ◽  
Protein Amino Acid ◽  
Protein Amino Acid Sequence ◽  
Self Association
Sign in / Sign up

Export Citation Format

Share Document