Accurate Determination of Interfacial Protein Secondary Structure by Combining Interfacial-Sensitive Amide I and Amide III Spectral Signals

2014 ◽  
Vol 136 (4) ◽  
pp. 1206-1209 ◽  
Author(s):  
Shuji Ye ◽  
Hongchun Li ◽  
Weilai Yang ◽  
Yi Luo
Keyword(s):  
Secondary Structure ◽  
Accurate Determination ◽  
Protein Secondary Structure

PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA

2010 ◽  
Vol 08 (05) ◽  
pp. 867-884 ◽  
Author(s):  
YUZHONG ZHAO ◽  
BABAK ALIPANAHI ◽  
SHUAI CHENG LI ◽  
MING LI
Keyword(s):  
Chemical Shift ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Nearest Neighbor ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Accurate Determination ◽  
Protein Secondary Structure ◽  
Sequence Information ◽  
K Nearest Neighbor

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q3 accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at .

Protein Secondary Structure Determination (PSSD): A New and Simple Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 246-253
Author(s):  
Anindya Sundar Panja ◽  
Bidyut Bandopadhyay ◽  
Akash Nag ◽  
Smarajit Maiti
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Computational Algorithm ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Dynamic Algorithm ◽  
Previous Algorithm ◽  
Function Relationship

Background: Our present investigation was conducted to explore the computational algorithm for the protein secondary structure prediction as per the property of evolutionary transient and large number (each 50) of homologous mesophilic-thermophilic proteins. </P><P> Objectives: These mesophilic-thermophilic proteins were used for numerical measurement of helix-sheetcoil and turn tendency for which each amino-acid residue is screened to build up the propensity-table. Methods: In the current study, two different propensity windows have been introduced that allowed predicting the secondary structure of protein more than 80% accuracy. Results: Using this propensity matrix and dynamic algorithm-based programme, a significant and decisive outcome in the determination of protein (both thermophilic and mesophilic) secondary structure was noticed over the previous algorithm based programme. It was demonstrated after comparison with other standard methods including DSSP adopted by PDB with the help of multiple comparisons ANOVA and Dunnett’s t-test. Conclusion: The PSSD is of great importance in the prediction of structural features of any unknown, unresolved proteins. It is also useful in the studies of proteins structure-function relationship.

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