SEGEM: a Fast and Accurate Automated Protein Backbone Structure Modeling Method for Cryo-EM

2021 ◽  
Author(s):  
Sheng Chen ◽  
Sen Zhang ◽  
Xiongjun Li ◽  
Yubao Liu ◽  
Yuedong Yang
Keyword(s):  
Modeling Method ◽  
Structure Modeling ◽  
Protein Backbone ◽  
Backbone Structure

scholarly journals MULTICOM2: an open-source protein structure prediction system powered by deep learning and distance prediction

2021 ◽  
Author(s):  
Tianqi Wu ◽  
Jian Liu ◽  
Zhiye Guo ◽  
Jie Hou ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Open Source ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Modeling Method ◽  
Structure Modeling ◽  
Prediction System ◽  
Template Free

Abstract Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system - MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0.

Research on Parameterized Modeling Technology for the Bolted Joints Structure

2014 ◽  
Vol 565 ◽  
pp. 211-216 ◽  
Author(s):  
Xue Zhai ◽  
Qing Gang Zhai ◽  
Jian Jun Wang ◽  
Xu Guang Fu
Keyword(s):  
Mechanical System ◽  
Mechanical Characteristics ◽  
Modeling Method ◽  
Bolted Joints ◽  
Structure Modeling ◽  
Theoretical Derivation ◽  
Turbofan Engine ◽  
Modeling Technology ◽  
Parameterized Modeling ◽  
Improve Calculation

Bolted joints structures exist in modern mechanical system, it is difficult to simulate the characteristics of the bolted joints accurately and effectively because of their complexity and diversity, which bring a great challenge to the whole structure modeling. Based on theoretical derivation of the bolted joints mechanical characteristics, the parameterized simulation technology and modeling method was used to equivalent simulate the bolted joints and with the example of a certain type of modern turbofan engine to elaborate the application of the parameterized modeling method. The research results have shown that the parameterized modeling method can not only simulate the true characteristics of bolted joints but also can improve calculation efficiency and saving computing cost effectively.

scholarly journals REDCRAFT: A tool for simultaneous characterization of protein backbone structure and motion from RDC data

2008 ◽  
Vol 191 (2) ◽  
pp. 322-334 ◽  
Author(s):  
Michael Bryson ◽  
Fang Tian ◽  
James H. Prestegard ◽  
Homayoun Valafar
Keyword(s):  
Protein Backbone ◽  
Structure And Motion ◽  
Backbone Structure
Sign in / Sign up

Export Citation Format

Share Document