scholarly journals Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO-FOLD

2010 ◽  
Vol 78 (8) ◽  
pp. 1825-1846 ◽  
Author(s):  
R. Rajgaria ◽  
Y. Wei ◽  
C. A. Floudas
Keyword(s):  
First Principles ◽  
Structure Prediction ◽  
Linear Optimization ◽  
3D Structure ◽  
Prediction Method ◽  
Contact Prediction ◽  
Beta Proteins ◽  
3D Structure Prediction ◽  
Alpha Beta ◽  
Structure Prediction Method

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 RNA 3D structure prediction guided by independent folding of homologous sequences

2019 ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz Bujnicki
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Prediction Method ◽  
3D Models ◽  
Target Sequence ◽  
Rna Sequences ◽  
Homologous Sequences ◽  
3D Structure Prediction ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction method. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates how important is the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identification of limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

scholarly journals RNA 3D structure prediction guided by independent folding of homologous sequences

2019 ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz Bujnicki
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Prediction Method ◽  
3D Models ◽  
Target Sequence ◽  
Rna Sequences ◽  
Homologous Sequences ◽  
3D Structure Prediction ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction method. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates how important is the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identification of limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

scholarly journals RNA 3D structure prediction guided by independent folding of homologous sequences

2019 ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz Bujnicki
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Prediction Method ◽  
3D Models ◽  
Target Sequence ◽  
Rna Sequences ◽  
Homologous Sequences ◽  
3D Structure Prediction ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction method. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates how important is the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identification of limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

Electronic structure and photocatalytic band offset of few-layer GeP2

2017 ◽  
Vol 5 (42) ◽  
pp. 22146-22155 ◽  
Author(s):  
Fazel Shojaei ◽  
Jae Ryang Hahn ◽  
Hong Seok Kang
Keyword(s):  
Crystal Structure ◽  
Electronic Structure ◽  
Structure Prediction ◽  
Prediction Method ◽  
Group Iv ◽  
Band Offset ◽  
Two Dimensional ◽  
Crystal Structure Prediction ◽  
T Phase ◽  
Structure Prediction Method

Based on a sophisticated crystal structure prediction method, we propose two-dimensional (2D) GeP2in the tetragonal (T) phase never observed for other group IV–V compounds.

scholarly journals CALYPSO structure prediction method

2015 ◽  
Vol 60 (27) ◽  
pp. 2580-2587 ◽  
Author(s):  
YanMing MA ◽  
Jian L ◽  
YanChao WANG
Keyword(s):  
Structure Prediction ◽  
Prediction Method ◽  
Structure Prediction Method

In silico 3D structure prediction and hydrogen peroxide binding study of wheat catalase

2013 ◽  
Vol 5 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Gopal Krishna Sahu ◽  
Bibhuti Bhusan Sahoo ◽  
Sneha Bhandari ◽  
Shruti Pandey
Keyword(s):  
Hydrogen Peroxide ◽  
In Silico ◽  
Structure Prediction ◽  
3D Structure ◽  
Binding Study ◽  
3D Structure Prediction
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