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

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 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 Improving Protein Structure Prediction by Smoothing Energy Functions

2010 ◽  
Vol 98 (3) ◽  
pp. 615a
Author(s):  
Justin L. MacCallum ◽  
Geoffrey C. Rollins ◽  
Ken A. Dill
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Energy Functions

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)

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