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dc.contributor.advisor Ma, Jianpeng
dc.creatorMa, Tianqi
dc.date.accessioned 2021-05-13T21:39:48Z
dc.date.available 2021-05-13T21:39:48Z
dc.date.created 2021-08
dc.date.issued 2021-05-13
dc.date.submitted August 2021
dc.identifier.urihttps://hdl.handle.net/1911/110602
dc.description.abstract Designing an efficient scoring function is one of the most challenging tasks in computational biology. A good potential functions or scoring functions can help rank protein structures models, guide the search and identify possible solutions. This is very important for protein structure prediction. A lot of work have been done in this area but none of them has achieved the desired result. It is therefore urgently needed to develop good scoring functions to accelerate the process. In this thesis, I will present several novel empirical potential functions and scoring functions to address this problem. First, an upgraded version of previous work OPUS-PSP, named OPUS-DOSP. A distance related term is added to the potential function and the performance is improved. Second, a non-traditional scoring function, named OPUS-CSF is developed. This scoring function didn’t use the traditional Boltzmann formula but constructed a native configuration distribution table instead. This scoring function outperformed the previous work, OPUS-DOSP. Thirdly, two scoring functions combining the features of previous two scoring functions are developed. OPUS-SSF and OPUS-Beta are their names. These two scoring functions yield the best result so far and are promising in this area. The effectiveness of these scoring functions is tested in various decoy sets generated from native structures. In the traditional benchmarks like ROSETTA, ig_structure, fisa_casp3, MOULDER and so on, OPUS-DOSP, OPUS-CSF, OPUS-SSF are performing better than previous works. In beta-prediction benchmarks Beta916 and Beta1452, OPUS-Beta also outperformed existing methods. Therefore, these scoring functions seem to be promising and useful in this field.
dc.format.mimetype application/pdf
dc.language.iso en
dc.subjectScoring function
empirical potential
protein structure recognition
side-chain packing
coarse-graining
dc.title A series of advanced scoring functions in ranking protein structures
dc.type Thesis
dc.date.updated 2021-05-13T21:39:49Z
dc.type.material Text
thesis.degree.department Applied Physics
thesis.degree.discipline Applied Physics/Bioengineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Ma, Tianqi. "A series of advanced scoring functions in ranking protein structures." (2021) Diss., Rice University. https://hdl.handle.net/1911/110602.


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