Prediction of temperature factors from protein sequence
Protein flexibility is useful in structural and functional aspect of proteins. We have analyzed the local primary protein sequence features that in combination can predict the B-value of amino acid residues directly from the protein sequence. We have also analyzed the distribution of B-value in diff...
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Biomedical Informatics
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3569600/ |
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pubmed-35696002013-02-19 Prediction of temperature factors from protein sequence Sonavane, Shrihari Jaybhaye, Ashok A Jadhav, Ajaykumar G Hypothesis Protein flexibility is useful in structural and functional aspect of proteins. We have analyzed the local primary protein sequence features that in combination can predict the B-value of amino acid residues directly from the protein sequence. We have also analyzed the distribution of B-value in different regions of protein three dimensional structures. On an average, the normalized Bvalue decreases by 0.1055 with every 0.5Å increase in the distance of the residue from protein surface. The residues in the loop regions have higher B-values as compared to the residues present in other regular secondary structural elements. Buried residues which are present in the protein core are more rigid (lower B-values) than the residues present on the protein surface. Similarly, the hydrophobic residues which tend to be present in the protein core have lower average B-value than the polar residues. Finally, we have proposed the method based on Support Vector Regression (SVR) to predict the B-value from protein primary sequence. Our result shows that, the SVR model achieved the correlation coefficient of 0.47 which is comparable to existing methods. Biomedical Informatics 2013-02-06 /pmc/articles/PMC3569600/ /pubmed/23422595 http://dx.doi.org/10.6026/97320630009134 Text en © 2013 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Sonavane, Shrihari Jaybhaye, Ashok A Jadhav, Ajaykumar G |
| spellingShingle |
Sonavane, Shrihari Jaybhaye, Ashok A Jadhav, Ajaykumar G Prediction of temperature factors from protein sequence |
| author_facet |
Sonavane, Shrihari Jaybhaye, Ashok A Jadhav, Ajaykumar G |
| author_sort |
Sonavane, Shrihari |
| title |
Prediction of temperature factors from protein sequence |
| title_short |
Prediction of temperature factors from protein sequence |
| title_full |
Prediction of temperature factors from protein sequence |
| title_fullStr |
Prediction of temperature factors from protein sequence |
| title_full_unstemmed |
Prediction of temperature factors from protein sequence |
| title_sort |
prediction of temperature factors from protein sequence |
| description |
Protein flexibility is useful in structural and functional aspect of proteins. We have analyzed the local primary protein sequence
features that in combination can predict the B-value of amino acid residues directly from the protein sequence. We have also
analyzed the distribution of B-value in different regions of protein three dimensional structures. On an average, the normalized Bvalue
decreases by 0.1055 with every 0.5Å increase in the distance of the residue from protein surface. The residues in the loop
regions have higher B-values as compared to the residues present in other regular secondary structural elements. Buried residues
which are present in the protein core are more rigid (lower B-values) than the residues present on the protein surface. Similarly, the
hydrophobic residues which tend to be present in the protein core have lower average B-value than the polar residues. Finally, we
have proposed the method based on Support Vector Regression (SVR) to predict the B-value from protein primary sequence. Our
result shows that, the SVR model achieved the correlation coefficient of 0.47 which is comparable to existing methods. |
| publisher |
Biomedical Informatics |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3569600/ |
| _version_ |
1611953988870078464 |