scholarly journals Protein Structure Prediction with Parallel Algorithms Orthogonal to Parallel Platforms

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Matheus Henrique Junqueira Saldanha ◽  
Paulo Sérgio Lopes De Souza
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
High Performance ◽  
Parallel Architectures ◽  
Computational Science ◽  
Programming Models ◽  
Performance Techniques ◽  
Parallel Programming Models ◽  
Computationally Expensive

The problem of Protein Structure Prediction (PSP) is known to be computationally expensive, which calls for the application of high performance techniques. In this project, parallel PSP algorithms found in the literature are being accelerated and ported to different parallel platforms, producing a set of algorithms that it is diverse in terms of the parallel architectures and parallel programming models used. The algorithms are intended to help other research projects and they have also been made publicly available so as to support the development of more elaborate prediction algorithms. We have thus far produced a set of 16 algorithms (mixing CUDA, OpenMP, MPI and/or complexity reduction optimizations); during its development, two algorithms that promote high performance were proposed, and they have been written in an article that was accepted in the International Conference on Computational Science (ICCS).

HPS_PSP: HIGH PERFORMANCE SYSTEM FOR PROTEIN STRUCTURE PREDICTION

2019 ◽  
Vol 27 (04) ◽  
pp. 487-502
Author(s):  
MOHAMED B. ABDELHALIM ◽  
MAI S. MABROUK ◽  
AHMED Y. SAYED
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
High Performance ◽  
Neighborhood Search ◽  
Efficient Design ◽  
Local Optima ◽  
Genetic Operator ◽  
Comparative Results ◽  
Prediction Approach

Prediction of least energy conformation of a protein from its primary structure (chain of amino acids) is an optimization problem associated with a large complex energy landscape. In this study, a simple 2D hydrophobic–hydrophilic model was used to model the protein sequence, which allows the fast and efficient design of genetic algorithm-based protein structure prediction approach. The neighborhood search strategy is integrated into the genetic operator. The neighborhood search guides the genetic operator to regions in the computational space with good solutions. To prevent convergence to local optima, the proposed method employs crowding-based parent replacement strategy, which improves the performance of the algorithm and the ability to deal with multiple numbers of solutions. The proposed algorithm was tested with a standard benchmark of HP sequences and comparative results demonstrate that the proposed system beats most of the evolutionary algorithms for seven sequences. It finds the best energy for a sequence of length [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text].

Comparing Parallel Algorithms for Van der Waals Energy with Cell-List Technique for Protein Structure Prediction

2018 ◽  
Author(s):  
Daniel R. F. Bonetti ◽  
Gesiel Rios Lopes ◽  
Alexandre C. B. Delbem ◽  
Paulo S. L. Souza ◽  
Kalinka C. Branco ◽  
...  
Keyword(s):  
Protein Structure ◽  
Parallel Algorithms ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Van Der Waals ◽  
Parallel Execution ◽  
Programming Models ◽  
Preliminary Results ◽  
Small Proteins

This paper compares the runtime of three distinct parallel algorithms for the evaluation of an ab initio and full-atom approach based on GA and celllist technique, in order to minimize the van der Waals energy. The three parallel algorithms are developed in C and use one of these programming models: MPI, OpenMP or hybrid (MPI+OpenMP). Our preliminary results show that van der Waals Energy are executed faster and with better speedups when using hybrid and more flexible parallel algorithms to predict the structure of larger proteins. We also show that for small proteins the communication of MPI imposes a high overhead for the parallel execution and, thus the OpenMP presents a better relation cost x benefit in such cases.

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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