How Good Are Simplified Models for Protein Structure Prediction?

Shatabda, Swakkhar; Newton, M. A. Hakim; Rashid, Mahmood A.; Pham, Duc Nghia; Sattar, Abdul

doi:10.1155/2014/867179

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…This indicates the absence of strong positive correlation between the value of the energy function and the measure. Similar findings are also reported in Shatabda et al [ 34 ]. Details of the energy function values and corresponding values are given in the electronic supplementary materials, table S4.…”

Section: Resultssupporting

confidence: 92%

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Efficient conformational space exploration inab initioprotein folding simulation

Ullah

Ahmed²,

Pappu³

et al. 2015

R. Soc. open sci.

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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.

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Section: Resultssupporting

confidence: 92%

“…Different discretized lattice models have been considered in the literature. Among those, the face centered cubic (FCC) lattice has been shown to yield very good approximations of real protein structures [ 33 , 34 ]. The domain of an FCC lattice consists of points

.…”

Section: Methodsmentioning

confidence: 99%

Efficient conformational space exploration inab initioprotein folding simulation

Ullah

Ahmed²,

Pappu³

et al. 2015

R. Soc. open sci.

Self Cite

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“…The best hydrophobic cores do not always correspond to the best structures in terms of RMSD values [ 67 , 68 ]. These observations inspired us to mix the energy models.…”

Section: Experimental Results and Analysesmentioning

confidence: 99%

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Rashid

Shatabda

Newton³

et al. 2014

Advances in Bioinformatics

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Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads.

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“…However, results of literature showed that in some cases lower energy values are not associated with lower RMSD values. Some works attribute this fact to the challenges imposed by the PSP problem and that further investigation is needed in the modeling of the problem, as in the case of force fields, for example [72,60].…”

Section: Algorithmmentioning

confidence: 99%

Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

Siqueira¹,

Venske²

2021

RITA

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Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific points of the algorithms used. A comparison between the approaches is presented and some directions of the research field are pointed out.

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How Good Are Simplified Models for Protein Structure Prediction?

Cited by 14 publications

References 40 publications

Efficient conformational space exploration inab initioprotein folding simulation

Efficient conformational space exploration inab initioprotein folding simulation

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

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