scholarly journals Reoptimized UNRES Potential for Protein Model Quality Assessment

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 601 ◽  
Author(s):  
Eshel Faraggi ◽  
Pawel Krupa ◽  
Magdalena Mozolewska ◽  
Adam Liwo ◽  
Andrzej Kloczkowski
Keyword(s):  
Protein Structure ◽  
Coarse Grained ◽  
Poor Correlation ◽  
Native Structure ◽  
Feed Forward Neural Network ◽  
Model Quality ◽  
Protein Model ◽  
Protein Model Quality Assessment ◽  
Dynamics Simulations ◽  
Degree Of Similarity

Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model’s Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. The reoptimized version of UNRES (OUNRES) is available at http://mamiris.com and http://www.unres.pl.

scholarly journals AngularQA: Protein Model Quality Assessment with LSTM Networks

2019 ◽  
Author(s):  
Matthew Conover ◽  
Max Staples ◽  
Dong Si ◽  
Miao Sun ◽  
Renzhi Cao
Keyword(s):  
Protein Structure ◽  
Quality Assessment ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Model Quality ◽  
Time Step ◽  
Testing Dataset ◽  
Protein Model Quality Assessment ◽  
Lstm Network ◽  
Protein Structure Prediction Problem

AbstractQuality Assessment (QA) plays an important role in protein structure prediction. Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure at each time-step, without using any database. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub:https://github.com/caorenzhi/AngularQA

scholarly journals Folding Trp-Cage to NMR Resolution Native Structure Using a Coarse-Grained Protein Model

2005 ◽  
Vol 88 (1) ◽  
pp. 147-155 ◽  
Author(s):  
Feng Ding ◽  
Sergey V. Buldyrev ◽  
Nikolay V. Dokholyan
Keyword(s):  
Coarse Grained ◽  
Native Structure ◽  
Protein Model
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