CFSSP is a online program which predicts secondary structure of the protein. In this program Chou & Fasman algorithm is implemented. This exercise teaches how to use the Chou-Fasman Interactive. The Chou- Fasman method predicts protein secondary structures in a given protein sequence. Predict locations of alpha-helix and beta-strand from amino acid sequence using Chou-Fasman method, Garnier-Osguthorpe-Robson method, and Neural.
|Published (Last):||1 November 2006|
|PDF File Size:||11.18 Mb|
|ePub File Size:||6.54 Mb|
|Price:||Free* [*Free Regsitration Required]|
Before performing our method, we compared traditional CFM proposed in with four current methods mentioned above to see how large the difference is. Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. The values obtained by Mandell et al. Unlike the more complex GOR methodit does not reflect the conditional probabilities of an amino acid to form a particular secondary structure given that its neighbors already possess that structure.
The prediction technique has been developed for several decades. The Chou-Fasman algorithm is simple in principle. Our method has solved two problems in CFM, the unreliable parameters and low accuracy. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.
Three rules have been proposed in CFM, including the locating of nucleation regions, extending nucleation regions, and the refinement of secondary structure segment [ 10 ]. H, G, and I are helices; E and B are strands; other conformations are coils. Our method has a great improvement in all of the indices compared with CFM, and the result of our method is comparable with current popular methods.
Finally, we realized the full-automation of our method for the analysis of great number of data set.
It was used to measure the accuracy of secondary structure segments [ 37 ]. That means the CFM is weak in hitting the protein secondary structure segment and it tends to over predict. Seventy-five percent accuracy in protein secondary structure prediction.
We xhou undid some processes in modification of the third rule. From these values, it can be found that many indices were no big difference but the SOV indices were improved increasingly.
CFSSP: Chou & Fasman Secondary Structure Prediction Server
In our method, we used the propensities which were computed based on statistics. This may be due to the small proportion of strand in alpha class and low proportion of helix in strand class.
Plenum Press, New York; For example, the thermodynamic method which was used in reference [ 17 ] and [ 18 ]. The Chou-Fasman algorithm, one of the algoritgm methods, has been successfully applied afsman the prediction. If we can hit every protein secondary structure segment nucleation, the result should be improved increasingly.
Because of its character of multi-resolution, WT has been applied in bioinformatics to analyze and process biological data [ 27 ] recently. Ning Qian and Cuou J.
Based on this hypothesis, the protein secondary and tertiary structures and their domains are contained within a peptide chain.
Thanks to the recent development in protein folding fasmann structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. Nevertheless, in our method, the accuracy of alpha class and beta class is still well.
Table 6 The degree of improvement with 3 different steps of our method. In folding type-specific structure propensities, there is no strand value in proteins choi all alpha class, while no helix value in all beta class.
A nucleation can be predicted when 4 of 6 sequential residues in certain segment tend to form helix the helix formerand this number is 3 of 5 for strand. Chou and Gerald D. We improved Chou-Fasman method in three aspects. The method was originally presented in and later improved in,and This is because the breaker such as proline was found to be existed in helix or strand of some proteins.
Conclusion In our method, CFM was improved with modifications in nucleation regions, parameters and some rules. Another index which was proposed recently is the SOV segment wlgorithm measure. This process is repeated througout the sequence until the entire sequence is predicted. People who are interested in this algorithm can contact us by sending an email requesting source code written in matlab language.
This value is 1 for both helix and strand in CFM, which is approximately the average propensity value of the 20 amino acids.
And the problem over prediction has been partially solved. Table 8 Result with all three improvements. The CB data set was tested by using improved Chou-Fasman method and three indices: Retrieved from ” https: Bioinformatics sequence and genome analysis. Wavelet transformation agorithm protein hydrophobicity sequences suggests their memberships in structural families.
However, the results calculated by different thresholds around average propensity value were very close in our test. At that point, the structure algoritmh terminated. If the first two conditions are met but the probability of a beta sheet p b exceeds p tthen a sheet is predicted instead.
It is also comparable with current popular methods in protein secondary structure prediction. Assessment of secondary-structure prediction of proteins comparison of computerized Chou-Fasman method with others.
We utilized some parameters concluded by other researchers [ 102028 ], and ensured that our test data set is different from their training data set. We reserved this modification because the SOV indices were considered more important in our method.
The extension rule is related not only with propensities, but also with the terminating threshold.