scholarly journals An improved protein structure evaluation using a semi-empirically derived structure property

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Manoj Kumar Pal ◽  
Tapobrata Lahiri ◽  
Garima Tanwar ◽  
Rajnish Kumar
Keyword(s):  
Protein Structure ◽  
Structure Property ◽  
Structure Evaluation

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)

Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation

2018 ◽  
Vol 14 (11) ◽  
pp. 6015-6025 ◽  
Author(s):  
Aliza B. Rubenstein ◽  
Kristin Blacklock ◽  
Hai Nguyen ◽  
David A. Case ◽  
Sagar D. Khare
Keyword(s):  
Protein Structure ◽  
Energy Functions ◽  
Systematic Comparison ◽  
Structure Evaluation

scholarly journals PROSESS: a protein structure evaluation suite and server

2010 ◽  
Vol 38 (Web Server) ◽  
pp. W633-W640 ◽  
Author(s):  
M. Berjanskii ◽  
Y. Liang ◽  
J. Zhou ◽  
P. Tang ◽  
P. Stothard ◽  
...  
Keyword(s):  
Protein Structure ◽  
Structure Evaluation

Systematic comparison of Amber and Rosetta energy functions for protein structure evaluation.

2018 ◽  
Author(s):  
Aliza Rubenstein ◽  
Kristin Blacklock ◽  
Hai Nguyen ◽  
David Case ◽  
Sagar Khare
Keyword(s):  
Protein Structure ◽  
Energy Function ◽  
Structure Prediction ◽  
Optimization Approach ◽  
Energy Landscapes ◽  
Loop Modeling ◽  
Native State ◽  
Energy Functions ◽  
Selection For ◽  
Structure Evaluation

<p>An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function, and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. Both Rosetta's talaris2014 and Amber's ff14SBonlySC energy functions performed well in scoring the native state as the lowest energy conformation in many cases. In 24/150 cases with Rosetta, and in 2/150 cases using Amber, a false minimum is found that is absent in the alternative landscape. In 21/150 cases, both energy function-generated landscapes featured false minima. The newest version of the Rosetta energy function, REF2015, which has more physically-derived terms than talaris2014, performs significantly better, highlighting the improvements made to the Rosetta scoring approach. To take advantage of the semi-orthogonal nature of these energy functions, we developed a Pareto optimization approach that combines Amber and Rosetta energy landscapes to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation, and should aid in model selection for structure prediction and loop modeling tasks. </p>

Systematic comparison of Amber and Rosetta energy functions for protein structure evaluation.

2018 ◽  
Author(s):  
Aliza Rubenstein ◽  
Kristin Blacklock ◽  
Hai Nguyen ◽  
David Case ◽  
Sagar Khare
Keyword(s):  
Protein Structure ◽  
Energy Function ◽  
Structure Prediction ◽  
Optimization Approach ◽  
Energy Landscapes ◽  
Loop Modeling ◽  
Native State ◽  
Energy Functions ◽  
Selection For ◽  
Structure Evaluation

<p>An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function, and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. Both Rosetta's talaris2014 and Amber's ff14SBonlySC energy functions performed well in scoring the native state as the lowest energy conformation in many cases. In 24/150 cases with Rosetta, and in 2/150 cases using Amber, a false minimum is found that is absent in the alternative landscape. In 21/150 cases, both energy function-generated landscapes featured false minima. The newest version of the Rosetta energy function, REF2015, which has more physically-derived terms than talaris2014, performs significantly better, highlighting the improvements made to the Rosetta scoring approach. To take advantage of the semi-orthogonal nature of these energy functions, we developed a Pareto optimization approach that combines Amber and Rosetta energy landscapes to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation, and should aid in model selection for structure prediction and loop modeling tasks. </p>

scholarly journals Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Mahmood A. Rashid ◽  
M. A. Hakim Newton ◽  
Md. Tamjidul Hoque ◽  
Abdul Sattar
Keyword(s):  
Protein Structure ◽  
High Resolution ◽  
Protein Structure Prediction ◽  
Energy Function ◽  
Structure Prediction ◽  
Energy Model ◽  
Low Resolution ◽  
Energy Models ◽  
Hydrophobic Cores ◽  
Structure Evaluation

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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