Efficient conformational space exploration in ab initio protein folding simulation
Ab initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2015-01-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.150238 |
| _version_ | 1818251267676307456 |
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| author | Ahammed Ullah Nasif Ahmed Subrata Dey Pappu Swakkhar Shatabda A. Z. M. Dayem Ullah M. Sohel Rahman |
| author_facet | Ahammed Ullah Nasif Ahmed Subrata Dey Pappu Swakkhar Shatabda A. Z. M. Dayem Ullah M. Sohel Rahman |
| author_sort | Ahammed Ullah |
| collection | DOAJ |
| description | Ab initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions are not very informative for search algorithms and fail to distinguish the types of amino acid interactions that contribute largely to the energy function from those that do not. As a result, search algorithms frequently get trapped into the local minima. On the other hand, the hydrophobic–polar (HP) model considers hydrophobic interactions only. The simplified nature of HP energy function makes it limited only to a low-resolution model. In this paper, we present a strategy to derive a non-uniform scaled version of the real 20×20 pairwise energy function. The non-uniform scaling helps tackle the difficulty faced by a real energy function, whereas the integration of 20×20 pairwise information overcomes the limitations faced by the HP energy function. Here, we have applied a derived energy function with a genetic algorithm on discrete lattices. On a standard set of benchmark protein sequences, our approach significantly outperforms the state-of-the-art methods for similar models. Our approach has been able to explore regions of the conformational space which all the previous methods have failed to explore. Effectiveness of the derived energy function is presented by showing qualitative differences and similarities of the sampled structures to the native structures. Number of objective function evaluation in a single run of the algorithm is used as a comparison metric to demonstrate efficiency. |
| first_indexed | 2024-12-12T16:05:34Z |
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| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-12T16:05:34Z |
| publishDate | 2015-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-f3ade4ccb9af4a7ebc387f9a60356dbf2022-12-22T00:19:20ZengThe Royal SocietyRoyal Society Open Science2054-57032015-01-012810.1098/rsos.150238150238Efficient conformational space exploration in ab initio protein folding simulationAhammed UllahNasif AhmedSubrata Dey PappuSwakkhar ShatabdaA. Z. M. Dayem UllahM. Sohel RahmanAb initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions are not very informative for search algorithms and fail to distinguish the types of amino acid interactions that contribute largely to the energy function from those that do not. As a result, search algorithms frequently get trapped into the local minima. On the other hand, the hydrophobic–polar (HP) model considers hydrophobic interactions only. The simplified nature of HP energy function makes it limited only to a low-resolution model. In this paper, we present a strategy to derive a non-uniform scaled version of the real 20×20 pairwise energy function. The non-uniform scaling helps tackle the difficulty faced by a real energy function, whereas the integration of 20×20 pairwise information overcomes the limitations faced by the HP energy function. Here, we have applied a derived energy function with a genetic algorithm on discrete lattices. On a standard set of benchmark protein sequences, our approach significantly outperforms the state-of-the-art methods for similar models. Our approach has been able to explore regions of the conformational space which all the previous methods have failed to explore. Effectiveness of the derived energy function is presented by showing qualitative differences and similarities of the sampled structures to the native structures. Number of objective function evaluation in a single run of the algorithm is used as a comparison metric to demonstrate efficiency.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.150238protein folding simulationprotein structure predictionenergy functionoptimizationdiscrete latticesgenetic algorithms |
| spellingShingle | Ahammed Ullah Nasif Ahmed Subrata Dey Pappu Swakkhar Shatabda A. Z. M. Dayem Ullah M. Sohel Rahman Efficient conformational space exploration in ab initio protein folding simulation Royal Society Open Science protein folding simulation protein structure prediction energy function optimization discrete lattices genetic algorithms |
| title | Efficient conformational space exploration in ab initio protein folding simulation |
| title_full | Efficient conformational space exploration in ab initio protein folding simulation |
| title_fullStr | Efficient conformational space exploration in ab initio protein folding simulation |
| title_full_unstemmed | Efficient conformational space exploration in ab initio protein folding simulation |
| title_short | Efficient conformational space exploration in ab initio protein folding simulation |
| title_sort | efficient conformational space exploration in ab initio protein folding simulation |
| topic | protein folding simulation protein structure prediction energy function optimization discrete lattices genetic algorithms |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.150238 |
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