Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure 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 resolution20×20energy 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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A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Advances in Bioinformatics ◽

10.1155/2014/985968 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17

Author(s):

Mahmood A. Rashid ◽

Swakkhar Shatabda ◽

M. A. Hakim Newton ◽

Md Tamjidul Hoque ◽

Abdul Sattar

Keyword(s):

Protein Structure ◽

Ab Initio ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Single Point ◽

Three Dimensional ◽

Energy Model ◽

Cubic Lattice ◽

Energy Models ◽

Dimensional Face

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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Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

Plant Tissue Culture and Biotechnology ◽

10.3329/ptcb.v19i2.5439 ◽

1970 ◽

Vol 19 (2) ◽

pp. 217-226

Author(s):

S. M. Minhaz Ud-Dean ◽

Mahdi Muhammad Moosa

Keyword(s):

Protein Structure ◽

Dihedral Angle ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Protein Structures ◽

Angle Distribution ◽

Ramachandran Plot ◽

Specific Data ◽

Specific Distribution ◽

Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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