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A Statistical Analysis and Approach to Protein Surface Modeling
Author(s) -
Doucette Luticha,
Craig Paul A.,
Halavin James
Publication year - 2012
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.26.1_supplement.978.9
Subject(s) - python (programming language) , surface (topology) , surface protein , computer science , computational science , gaussian , algorithm , source code , accessible surface area , mathematics , chemistry , geometry , programming language , computational chemistry , biology , virology
In studying protein‐protein interactions it is important to accurately describe the surface of the proteins, where the interactions occur. Surface can be represented in two ways in RasMol ‐ as Lee‐ Richards surfaces which are generated by rolling a solvent molecule on the surface of the protein, or as an approximation to the Lee‐Richards surface by adding pseudo‐Gaussian electron densities. The second approach is a reasonable approximation to a Lee‐Richards surface with less computational burden. Either approach allows the user to identify atoms that are on the surface of the protein. A new algorithm for surface modeling was developed to improve speed and accuracy. In this approach protein surfaces are defined by their individual atoms as data points. The algorithm was implemented first in Minitab, then converted to Python code to increase speed. The surface atoms are identified by both Minitab and Python, but there are discrepancies between the output of the two programs. Further work will be done to resolve these discrepancies, the code will be converted to GPU processing to further increase the speed, and continued analysis on the trends seen with how many atoms are found on the outside and their biological significance. Thanks to the NSF LSAMP at RIT for funding this research.