scholarly journals A-Prot: Protein structure modeling using MSA transformer

2021 ◽  
Author(s):  
Yiyu Hong ◽  
Junsu Ko ◽  
Juyong Lee
Keyword(s):  
State Of The Art ◽  
3D Structure ◽  
Computational Cost ◽  
Structure Modeling ◽  
Protein Structure Modeling ◽  
3D Structure Modeling ◽  
Low Computational Cost ◽  
New Protein ◽  
Better Than ◽  
Infor Mation

In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein lan-guage models. For a given MSA, an MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14. These results imply that A-Prot captures evolutionary and structural infor-mation of proteins accurately with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein prop-erty prediction methods.

scholarly journals RNA and protein 3D structure modeling: similarities and differences

2011 ◽  
Vol 17 (9) ◽  
pp. 2325-2336 ◽  
Author(s):  
Kristian Rother ◽  
Magdalena Rother ◽  
Michał Boniecki ◽  
Tomasz Puton ◽  
Janusz M. Bujnicki
Keyword(s):  
3D Structure ◽  
Structure Modeling ◽  
Protein 3D Structure ◽  
3D Structure Modeling ◽  
Similarities And Differences

scholarly journals 3D Structure Modeling of Catalase Enzyme from Aspergillus fumigatus

2016 ◽  
Vol 1 (1) ◽  
pp. 008-012
Author(s):  
D Vivek Dhar ◽  
B Shiv ◽  
AC Kaushik ◽  
M Sarad Kumar
Keyword(s):  
Aspergillus Fumigatus ◽  
3D Structure ◽  
Structure Modeling ◽  
3D Structure Modeling

scholarly journals Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning

2020 ◽  
Author(s):  
Jin Li ◽  
Jinbo Xu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Prediction Method ◽  
Structure Modeling ◽  
Contact Prediction ◽  
Real Value ◽  
3D Structure Modeling ◽  
Distance Prediction

AbstractInter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then employ a novel method to fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.

scholarly journals A New Generalized Projection and Its Application to Acceleration of Audio Declipping

Axioms ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 105
Author(s):  
Pavel Rajmic ◽  
Pavel Záviška ◽  
Vítězslav Veselý ◽  
Ondřej Mokrý
Keyword(s):  
Signal Processing ◽  
Convex Optimization ◽  
Linear Operator ◽  
Explicit Formula ◽  
Convex Sets ◽  
State Of The Art ◽  
Computational Cost ◽  
The Other ◽  
Speed Up ◽  
Low Computational Cost

In convex optimization, it is often inevitable to work with projectors onto convex sets composed with a linear operator. Such a need arises from both the theory and applications, with signal processing being a prominent and broad field where convex optimization has been used recently. In this article, a novel projector is presented, which generalizes previous results in that it admits to work with a broader family of linear transforms when compared with the state of the art but, on the other hand, it is limited to box-type convex sets in the transformed domain. The new projector is described by an explicit formula, which makes it simple to implement and requires a low computational cost. The projector is interpreted within the framework of the so-called proximal splitting theory. The convenience of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.

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