2D Hydrophobic-polar Model Research Articles

Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic–Polar model

Hydrophobic–polar (HP) models are widely used to predict protein folding and hydrophobic interactions. Numerous optimization algorithms have been proposed to predict protein folding using the two-dimensional (2D) HP model. However, to obtain an optimal protein structure from the 2D HP model remains challenging. In this study, an algorithm integrating particle swarm optimization (PSO) and Tabu search (TS), named PSO–TS, was proposed to predict protein structures based on the 2D HP model. TS can help PSO to avoid getting trapped in a local optima and thus to remove the limitation of PSO in predicting protein folding by the 2D HP model. In this study, a total of 28 protein sequences were used to evaluate the accuracy of PSO–TS in protein folding prediction. The proposed PSO–TS method was compared with 15 other approaches for predicting short and long protein sequences. Experimental results demonstrated that PSO–TS provides a highly accurate, reproducible, and stabile prediction ability for the protein folding by the 2D HP model.

Hybrid High Exploration Particle Swarm Optimization Algorithm Improves the Prediction of the 2-Dimensional Hydrophobic-Polar Model for Protein Folding

Background: Protein folding depends on the nature of the amino acid sequence. Once folding process of the amino acid sequence is successful, the protein becomes functional. Recently, a two-dimensional hydrophobic-polar (2D HP) model algorithm has been developed for the effective prediction of protein folding. However, the particular 2D HP models still lack an algorithm for protein folding prediction. Objective: Some developed algorithms still require further improvement in terms of accuracy and search stability. Method: In order to evaluate its improvement for protein folding of the 2D HP model in this study, we propose the hybrid high exploration particle swarm optimization (HHEPSO) method, which employs the HEPSO algorithm for optimization which combines both hill climbing and greedy algorithms for local search. Results: Several algorithms for protein structure prediction on the 2D square and triangular lattice models are compared with HHEPSO. In terms of accuracy and stability, our proposed HHEPSO revealed better performance than most of the test algorithms. HHEPSO also successfully deals with protein structure prediction problems for the longer amino acid sequences. Conclusion: Our proposed HHEPSO algorithm is accurate and effective for protein structure prediction for a 2D triangular lattice model. Keywords: Hybrid algorithm, HHEPSO, particle swarm optimization, hill climbing algorithm, greedy algorithm, protein folding, hydrophobic-polar (HP) model.

Detailed folding structures of M-lycotoxin-Hc1a and its mutageneses using 2D HP model

Although the hydrophobic-polar (HP) model was proposed a decade ago, it applies almost to no real-case study because of its intense computation. In this study, a 2D HP model was applied to study the folding structures of M-lycotoxin-Hc1a, an antimicrobial peptide, in order to get full pictures of its numerous folding structures. The normalised hydrophobicity index was used to convert M-lycotoxin-Hc1a and its six mutageneses into HP sequences, and then the 2D HP model was used to compute all the possible folding structures (324 = 282,429,536,481), and finally the normalised hydrophobicity index was used to distinguish the native state. The results showed that M-lycotoxin-Hc1a had 6 and 138 folding structures at their native state with the minimal energy of − 13 at pH 2 and pH 7 when glycine served as hydrophobic amino acid. When glycine serves as polar amino acid, M-lycotoxin-Hc1a had 12 and 54 folding structures at their native state with the minimal energy of − 12 and − 13 at pH 2 and pH 7, respectively. This study advanced the knowledge on how to apply the HP model to real-life study, and how the mutageneses influenced the folding structures of M-lycotoxin-Hc1a, their native states and minimal energy at different pH levels.

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem

BackgroundThe protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model – the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model.ResultsWe present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations.ConclusionsThe application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise.