scholarly journals Analysis of distance-based protein structure prediction by deep learning in CASP13

2019 ◽  
Author(s):  
Jinbo Xu ◽  
Sheng Wang
Keyword(s):  
Deep Learning ◽  
3D Modeling ◽  
Structure Prediction ◽  
3D Structure ◽  
3D Models ◽  
Evolutionary Information ◽  
Structure Modeling ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
3D Structure Modeling

AbstractThis paper reports the CASP13 results of distance-based contact prediction, threading and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by us for contact prediction in CASP12. On the 32 CASP13 FM (free-modeling) targets with a median MSA (multiple sequence alignment) depth of 36, RaptorX yielded the best contact prediction among 46 groups and almost the best 3D structure modeling among all server groups without time-consuming conformation sampling. In particular, RaptorX achieved top L/5, L/2 and L long-range contact precision of 70%, 58% and 45%, respectively, and predicted correct folds (TMscore>0.5) for 18 of 32 targets. Although on average underperforming AlphaFold in 3D modeling, RaptorX predicted correct folds for all FM targets with >300 residues (T0950-D1, T0969-D1 and T1000-D2) and generated the best 3D models for T0950-D1 and T0969-D1 among all groups. This CASP13 test confirms our previous findings: (1) predicted distance is more useful than contacts for both template-based and free modeling; and (2) structure modeling may be improved by integrating alignment and co-evolutionary information via deep learning. This paper will discuss progress we have made since CASP12, the strength and weakness of our methods, and why deep learning performed much better in CASP13.

scholarly journals AttentiveDist: Protein Inter-Residue Distance Prediction Using Deep Learning with Attention on Quadruple Multiple Sequence Alignments

2020 ◽  
Author(s):  
Aashish Jain ◽  
Genki Terashi ◽  
Yuki Kagaya ◽  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Charles Christoffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Prediction Models ◽  
3D Structure ◽  
Evolutionary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Distance Prediction

ABSTRACTProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. The model is trained in a multi-task fashion to also predict backbone and orientation angles further improving the inter-residue distance prediction. We show that AttentiveDist outperforms the top methods for contact prediction in the CASP13 structure prediction competition. To aid in structure modeling we also developed two new deep learning-based sidechain center distance and peptide-bond nitrogen-oxygen distance prediction models. Together these led to a 12% increase in TM-score from the best server method in CASP13 for structure prediction.

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 Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aashish Jain ◽  
Genki Terashi ◽  
Yuki Kagaya ◽  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Charles Christoffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
Evolutionary Information ◽  
Learning Approaches ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Novel Approach

AbstractProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.

scholarly journals Performance Analysis of Deep Learning Methods for Protein Contact Prediction in CASP13

2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Romina Valdez ◽  
Khevin Roig ◽  
Diego P. Pinto-Roa ◽  
Jose Colbes
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Dimensional Structure ◽  
Structural Classification ◽  
Data Set ◽  
Contact Prediction

Protein structure prediction is one of the most important problems in Computational Biology; and consists of determining the 3D structure of a protein given its amino acid sequence. A key component that has allowed considerable improvements in recent decades is the prediction of contacts in a protein, since it provides fundamental information about its three-dimensional structure. In the 13th edition of the CASP (Critical Assessment of protein Structure Prediction), a notable progress has been evidenced for both problems with the use of deep learning algorithms. For the contact prediction category, the best methods in CASP13 achieved an average precision of 70%. In the present work, the performance of these methods is analyzed using a larger data set, with 483 proteins from four families according to the structural classification of the SCOP database (Structural Classification of Proteins). The selected methods were evaluated using the CASP metrics, and their results indicate an average contact prediction precision greater than 90%. SPOT-Contact was the method with the best overall performance, and one of the methods with the best performance for each SCOP class. The set of proteins used for the experiments and the implementations made for the analysis are publicly available.

Optimization of production conditions and 3D-structure modeling of novel antibacterial peptide of lantibiotic family

2017 ◽  
Vol 53 (1) ◽  
pp. 40-46 ◽  
Author(s):  
T. V. Polyudova ◽  
L. M. Lemkina ◽  
G. N. Likhatskaya ◽  
V. P. Korobov
Keyword(s):  
3D Structure ◽  
Antibacterial Peptide ◽  
Structure Modeling ◽  
3D Structure Modeling ◽  
Production Conditions

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

Corrigendum to “3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design” [Biochem. Biophys. Res. Commun. 355 (2007) 513–519]

2007 ◽  
Vol 357 (1) ◽  
pp. 330 ◽  
Author(s):  
Jing-Fang Wang ◽  
Dong-Qing Wei ◽  
Lin Li ◽  
Si-Yuan Zheng ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Drug Design ◽  
3D Structure ◽  
Structure Modeling ◽  
Cytochrome P450 2C19 ◽  
3D Structure Modeling

RNA 3D Structure Modeling by Combination of Template-Based Method ModeRNA, Template-Free Folding with SimRNA, and Refinement with QRNAS

2016 ◽  
pp. 217-235 ◽  
Author(s):  
Pawel Piatkowski ◽  
Joanna M. Kasprzak ◽  
Deepak Kumar ◽  
Marcin Magnus ◽  
Grzegorz Chojnowski ◽  
...  
Keyword(s):  
3D Structure ◽  
Structure Modeling ◽  
3D Structure Modeling ◽  
Template Free

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

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz M. Bujnicki
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
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. 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 the importance of 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 identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

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