scholarly journals How Good Are Simplified Models for Protein Structure Prediction?

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Swakkhar Shatabda ◽  
M. A. Hakim Newton ◽  
Mahmood A. Rashid ◽  
Duc Nghia Pham ◽  
Abdul Sattar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Protein Chain ◽  
Face Centered Cubic ◽  
Energy Functions ◽  
Energy Models ◽  
Discrete Lattices ◽  
Face Centered

Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP.

scholarly journals A Sequential Niche Multimodal Conformation Sampling Algorithm for Protein Structure Prediction

2020 ◽  
Author(s):  
Yu-Hao Xia ◽  
Chun-Xiang Peng ◽  
Xiao-Gen Zhou ◽  
Gui-Jun Zhang
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
High Energy ◽  
Native Structure ◽  
Energy Barriers ◽  
Energy Functions ◽  
Sampling Algorithm ◽  
Energy Models ◽  
Protein Energy

AbstractMotivationMassive local minima on the protein energy surface often causes traditional conformation sampling algorithms to be easily trapped in local basin regions, because they are difficult to stride over high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy.ResultsA sequential niche multimodal conformation sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm stride over high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high- energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins and 24 CASP13 FM targets. Results show that SNfold is comparable with Rosetta restrained by distance (Rosetta-dist) and C-QUARK. SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta-dist protocol, SNfold achieves higher average TM- score and improves the sampling efficiency by more than 100 times. On the 24 CASP13 FM targets, SNfold is also comparable with four state-of-the-art methods in the CASP13 server group. As a plugin conformation sampling algorithm, SNfold can be extended to other protein structure prediction methods.AvailabilityThe source code and executable versions are freely available at https://github.com/iobio-zjut/[email protected]

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 An improved protein decoy set for testing energy functions for protein structure prediction

2003 ◽  
Vol 53 (1) ◽  
pp. 76-87 ◽  
Author(s):  
Jerry Tsai ◽  
Richard Bonneau ◽  
Alexandre V. Morozov ◽  
Brian Kuhlman ◽  
Carol A. Rohl ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Energy Functions
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