An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction

2010 ◽  
pp. 38-49 ◽  
Author(s):  
Camelia Chira ◽  
Dragos Horvath ◽  
Dumitru Dumitrescu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Evolutionary Model ◽  
Hill Climbing ◽  
Model Based

scholarly journals Improving protein structure prediction with model-based search

2005 ◽  
Vol 21 (Suppl 1) ◽  
pp. i66-i74 ◽  
Author(s):  
T J Brunette ◽  
O. Brock
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Model Based

scholarly journals A Novel Framework for Ab Initio Coarse Protein Structure Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sandhya Parasnath Dubey ◽  
S. Balaji ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Search Space ◽  
Population Based ◽  
Hill Climbing ◽  
Superior Performance ◽  
Protein Database ◽  
Fitness Value

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

scholarly journals EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION

2007 ◽  
Vol 05 (02a) ◽  
pp. 335-352 ◽  
Author(s):  
KEVIN W. DERONNE ◽  
GEORGE KARYPIS
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Query Sequence ◽  
Optimization Techniques ◽  
Hill Climbing ◽  
Prediction Algorithm ◽  
Scoring Functions ◽  
Fragment Assembly ◽  
Fold Prediction

Despite recent developments in protein structure prediction, an accurate new fold prediction algorithm remains elusive. One of the challenges facing current techniques is the size and complexity of the space containing possible structures for a query sequence. Traditionally, to explore this space fragment assembly approaches to new fold prediction have used stochastic optimization techniques. Here, we examine deterministic algorithms for optimizing scoring functions in protein structure prediction. Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing (HC) algorithm. The main difference between the two is that the latter implements a technique to overcome local minima. Experiments on a diverse set of 276 proteins show that the HC algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique) in optimizing the root mean squared deviation between native and working structures.

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

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)

Protein structure prediction of human connexin 30 and its mutations in hearing system

2014 ◽  
Vol 3 (5) ◽  
Author(s):  
S. Reiisi ◽  
M. Hashemzade-chaleshtori ◽  
S. Reisi ◽  
H. Shahi ◽  
S. Parchami ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Connexin 30 ◽  
Hearing System
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