Harmony Search Algorithms For Ab Initio Protein Tertiary Structure Prediction
| dc.contributor.author | Abual-Rub, Mohammed Said Saleh | |
| dc.date.accessioned | 2019-10-04T02:06:30Z | |
| dc.date.available | 2019-10-04T02:06:30Z | |
| dc.date.issued | 2011-02 | |
| dc.description.abstract | Predicting the tertiary structure of proteins from their linear sequence is really a big challenge in biology. This thesis considers the ab initio protein tertiary structure prediction. The Harmony Search Algorithm (HSA) has been adapted for the protein structure prediction by modeling the problem as an optimization problem. HSA has obtained feasible solutions but not as magnificent as those reported in the literature. However, some shortcomings were identified and addressed by proposing an Adaptive Harmony Search Algorithm (AHSA) and a Hybrid Harmony Search Algorithm (HHSA). The AHSA introduces a new scheme for controlling the two main parameters of HSA, i.e. Pitch Adjustment Rate (PAR) and Harmony Memory Consideration Rate (HMCR), suitable for the Protein Structure Prediction Problem (PSPP). Experiments on two popular benchmarks namely ‘Met-enkephalin’ and ‘1CRN’ has been performed. The experimental results have proved that both AHSA and HHSA have improved the overall performance of ab initio protein tertiary structure prediction. Both AHSA and HHSA have converged the lowest energy of the given proteins, and their results have outperformed some of the lowest energies recorded by some state-of-the-art algorithms. Moreover, two new global optimal energy values of the the ‘Met-enkephalin’ protein has been recorded by both AHSA and HHSA based on ECEPP/3 and ECEPP/2 force fields with w = 180 . | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/8937 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Harmony Search Algorithms | en_US |
| dc.subject | Ab Initio Protein | en_US |
| dc.title | Harmony Search Algorithms For Ab Initio Protein Tertiary Structure Prediction | en_US |
| dc.type | Thesis | en_US |
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