An efficient encoding for simplified protein structure prediction using genetic algorithms

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

doi:10.1109/cec.2013.6557704

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…However, using 3D FCC lattice points, the recent stateof-the-art results for HP energy models have been achieved by genetic algorithms [10,37], local search approaches [8,11], a local search embedded GA [38], and a multipoint parallel local search approach [39].…”

Section: Hp Energy Basedmentioning

confidence: 99%

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Rashid

Newton²,

Hoque³

et al. 2013

BioMed Research International

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Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20 × 20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.

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Section: Hp Energy Basedmentioning

confidence: 99%

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Rashid

Newton²,

Hoque³

et al. 2013

BioMed Research International

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“…The Q3 accuracy as well as MCC measurements are accuracy indexes of prediction for every individual amino acid. Repeatedly the α-helices and β-sheets are composed of many adjacent amino acids sequences [11,12]. The high prediction accuracy of every single residue does not guarantee the accuracy of secondary structure prediction is also relatively high.…”

Section: Related Backgroundmentioning

confidence: 99%

Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction

Rout¹,

Mishra²,

Sahoo³

2021

RIA

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The protein secondary structure prediction (PSP) of the large biological molecule protein is an important task of bioinformatics and in the last decades many machines learning and soft computing methodologies play vital roles in achieving satisfactory results. The protein structural class determination is an important topic in protein science because an idea about protein structural class is quite useful to know about the changes and reaction of a living body in order to design new drugs and medicines. Though several hard computing techniques may be helpful in these areas but focusing upon the steady development and big data size in protein sequences that are entering into databanks, it is a challenge to do experiments with the hard computing techniques. Soft computing techniques like Artificial Neural Network, Fuzzy logic, Genetic Algorithm play a vital role for these types of genomic researches. To face these complex challenges, this article presents a novel method to predict the protein structure by using Genetic Algorithm. The Q3 accuracy and SOV measure analysis with SOVH, SOVE, SOVC value of respective α-helix (H), β-sheet (E) and coil/loop(C) structures are also discussed. The application of Genetic algorithm i.e. the proposed technique GApred provides better result than that of SPIDER2, JPred4, FSVM and SSpro5 for all the three datasets in the experiment. This method is helpful for distinct protein secondary structure prediction and a significant success rate was observed, which indicates that it can be used as a powerful tool in drug design and medicine research.

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“…However, using 3D FCC lattice points, the recent state-of-the-art results for the HP energy model have been achieved by genetic algorithms [51,52], local search approaches [38,53], a local search embedded GA [54], and a multi-point parallel local search approach [55]. Kern and Lio [56] applied hydrophobic-core guided genetic operator for efficient searching on HP, HPNX and hHPNX lattice models.…”

Section: Introductionmentioning

confidence: 99%

Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction

Rashid

Iqbal

Khatib

et al. 2016

Computational Biology and Chemistry

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Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy-strategically mixes the Miyazawa-Jernigan (MJ) energy with the hydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformational search. In our application, we introduced a 2 × 2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20 × 20 MJ energy model-the ultimate objective function of our GA that needs to be minimized-considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.

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An efficient encoding for simplified protein structure prediction using genetic algorithms

Cited by 11 publications

References 38 publications

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction

Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction

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