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Efficient conformational space exploration in ab initio protein folding simulation.

Ullah A, Ahmed N, Pappu SD, Shatabda S, Ullah AZ, Rahman MS - R Soc Open Sci (2015)

Bottom Line: As a result, search algorithms frequently get trapped into the local minima.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.

View Article: PubMed Central - PubMed

Affiliation: AℓEDA Group, Department of CSE , BUET , ECE Building, Dhaka 1205, Bangladesh ; Department of CSE , Independent University , Bangladesh, Dhaka 1229, Bangladesh.

ABSTRACT
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.

No MeSH data available.


Differences of average energy obtained by using derived energy function (GW) and using real energy function (BM).
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RSOS150238F4: Differences of average energy obtained by using derived energy function (GW) and using real energy function (BM).

Mentions: To illustrate the effectiveness of our GW energy function we run the same set of experiments using the real energy function (BM) to guide the genetic algorithm. Throughout the experiments, the same algorithmic settings are used. The difference is only in the search guidance. Figures 4 and 5 illustrate the differences in average and best energy levels achieved by the same genetic algorithm using different guidance. The improvement achieved by the genetic algorithm using GW energy function is shown for each of the proteins for different cut-off times. As is evident from the figures, the differences of both best and average values plotted against the protein lengths form downward sloping trend lines. These differences clearly denote the superiority of the GW energy function over the BM energy function in search guidance. Also, the differences become more pronounced when the dimensionality or the size of the protein sequence increases. This is also an indication of the scalability of our approach. The exact values to generate these figures and average, best, median, standard deviation of the energy levels for both GW-guided and BM-guided algorithms are presented in the electronic supplementary material, table S3.Figure 4.


Efficient conformational space exploration in ab initio protein folding simulation.

Ullah A, Ahmed N, Pappu SD, Shatabda S, Ullah AZ, Rahman MS - R Soc Open Sci (2015)

Differences of average energy obtained by using derived energy function (GW) and using real energy function (BM).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555859&req=5

RSOS150238F4: Differences of average energy obtained by using derived energy function (GW) and using real energy function (BM).
Mentions: To illustrate the effectiveness of our GW energy function we run the same set of experiments using the real energy function (BM) to guide the genetic algorithm. Throughout the experiments, the same algorithmic settings are used. The difference is only in the search guidance. Figures 4 and 5 illustrate the differences in average and best energy levels achieved by the same genetic algorithm using different guidance. The improvement achieved by the genetic algorithm using GW energy function is shown for each of the proteins for different cut-off times. As is evident from the figures, the differences of both best and average values plotted against the protein lengths form downward sloping trend lines. These differences clearly denote the superiority of the GW energy function over the BM energy function in search guidance. Also, the differences become more pronounced when the dimensionality or the size of the protein sequence increases. This is also an indication of the scalability of our approach. The exact values to generate these figures and average, best, median, standard deviation of the energy levels for both GW-guided and BM-guided algorithms are presented in the electronic supplementary material, table S3.Figure 4.

Bottom Line: As a result, search algorithms frequently get trapped into the local minima.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.

View Article: PubMed Central - PubMed

Affiliation: AℓEDA Group, Department of CSE , BUET , ECE Building, Dhaka 1205, Bangladesh ; Department of CSE , Independent University , Bangladesh, Dhaka 1229, Bangladesh.

ABSTRACT
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.

No MeSH data available.