A novel ab-initio genetic-based approach for protein folding prediction

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.

show abstract

“…if mutation( ) then (10) foreach ∈ do (11) newP.add(mutConf( )) (12) else (13) while ¬ full( ) do (14) , ← randomConfs(…”

Section: Exhaustive Generationmentioning

confidence: 99%

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

Rashid

Newton²,

Hoque³

et al. 2013

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…The first seven proteins (4RXN,1ENH, 4PTI, 2IGD, 1YPA, 1R69, and 1CTF) in Table 3 are taken from [62] and [65], and the next five proteins (3MX7, 3NBM, CMQO, 3MRO, and 3PNX) are taken from [65]. The two other protein instances in Table 5 (2J61 and 2HFQ) are taken from [61]. n/a n/a n/a n/a -92.61 -84.60 n/a 3MR7 189 88 n/a n/a n/a n/a -93.65 -83.93 n/a 3MQZ 215 115 n/a n/a n/a n/a -104.29 -95.22 2:00:00 n/a 3NO3 238 102 n/a n/a n/a n/a -122.97 -108.70 n/a 3NO6 248 112 n/a n/a n/a n/a -133.95 -117.11 n/a 3ON7 280 135 n/a n/a n/a n/a -116.88 -96.64 -n/a n/a denotes the experimental results are not available.…”

Section: Benchmarkmentioning

confidence: 99%

“…Several approaches towards the 20 × 20 energy model include a constraint programming technique used in [57,58] by to predict tertiary structures of real proteins using secondary structure information, a fragment assembly method [59] to optimize protein structures. Among other successful approaches, a population based local search [60] and a population based genetic algorithm [61] are found in the literature that applied empirical energy functions.…”

Section: Introductionmentioning

confidence: 99%

“…On a set of standard benchmark proteins, our MH model guided GA, named as MH GeneticAlgorithm (MH GA), shows significant improvements in terms of interaction energies and root-mean-square deviations in comparison to the state-of-the-art search approaches [62,65,61] for the lattice based PSP models. For a fair comparison, we run [62] and [65] using MJ energy model and in the result section, we compare our experimental results with the results produced by [62] and [65].…”

Section: Introductionmentioning

confidence: 99%

“…The benchmark proteins used in our experiments.In the literature we found very few works[60,61] that used 20 × 20 MJ potential-matrix[10] for protein structure prediction on 3D FCC lattice. However, Torres et al[61] used 3D HCP lattice and Kapsokalivas…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Rashid

Iqbal

Khatib

et al. 2016

Computational Biology and Chemistry

View full text Add to dashboard Cite

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.

show abstract

Neighborhood Selection in Constraint-Based Local Search for Protein Structure Prediction

Shatabda

Newton

Sattar

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Protein structure prediction (PSP) is a very challenging constraint optimization problem. Constraint-based local search approaches have obtained promising results in solving constraint models for PSP. However, the neighborhood exploration policies adopted in these approaches either remain exhaustive or are based on random decisions. In this paper, we propose heuristics to intelligently explore only the promising areas of the search neighborhood. On face centered cubic lattice using a realistic 20 × 20 energy model and standard benchmark proteins, we obtain structures with significantly lower energy and RMSD values than those obtained by the state-of-the-art algorithms.

show abstract

A novel ab-initio genetic-based approach for protein folding prediction

Cited by 9 publications

References 13 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

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

Neighborhood Selection in Constraint-Based Local Search for Protein Structure Prediction

Contact Info

Product

Resources

About